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-IMG_SIZE = 70 # specify on which size we want to transform the photos for example 50x50px +CATEGORIES = ['Full', 'Empty'] # labels +IMG_SIZE = 150 # specify on which size we want to transform the photos for example 50x50px NAME = 'plate-cnn-64x2-{}'.format(int(time.time())) training_data = [] X = [] # feature set y = [] # label set +''' +layer size | conv leyer | + 64 | 1 | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692 + 64 | 2 | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462 + 64 | 3 | +''' + def create_training_data(): for category in CATEGORIES: @@ -115,7 +122,7 @@ def creat_model(): model.fit(X, y, batch_size=32, epochs=10, validation_split=0.1, callbacks=[tenserboard]) - model.save(relative_path + 'SavedModels/plate-64x2-cnn.model') + model.save(relative_path + 'SavedModels/{}.model'.format(NAME)) def use_model_to_predict(name): @@ -128,7 +135,7 @@ def use_model_to_predict(name): plt.show() return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) - model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-64x2-cnn.model') + model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-cnn-64x1-1590497461.model') prediction = model.predict([prepare(relative_path + 'TestData/' + name + '.jpg')]) print(prediction) @@ -139,7 +146,7 @@ def use_model_to_predict(name): def text_speech(font: str, size: int, text: str, color, background, x, y, bold: bool, bubble: bool, screen): global textRect font = pygame.font.SysFont(None, size) - #font = pygame.font.Font(font, size) + # font = pygame.font.Font(font, size) font.set_bold(bold) textSurf = font.render(text, True, color).convert_alpha() if bubble: @@ -155,6 +162,7 @@ def text_speech(font: str, size: int, text: str, color, background, x, y, bold: textRect.center = (x, y) screen.blit(textSurf, textRect) + # save_dataset() -# use_model_to_predict('test-full-5') -# creat_model() +#use_model_to_predict('test-0') +creat_model()